We evaluate our model on two multi-turn dialog datasets: Microsoft Dialogue Challenge dataset (MDC) [1]} and Schema-Guided Dialogue dataset (SGD) [2]}. MDC is a human-annotated conversation dataset in three domains (movie, restaurant, taxi). Each of them contains 2890, 4103, 3094 dialogs with total 11 acts and 50 slots. For each utterance, it is attached one or more dialog acts and slots. SGD consists of over 20k annotated multi-domain, task-oriented conversations between a human and a virtual assistant. These conversations span 20 domains, ranging from banks and events to media, travel and weather. We mainly adopt the user and system acts in each utterance for the dialog act detection and corresponding slots for slot filling. It has total 18 acts and 89 slots. We mainly split the training/validation/testing data with the ratio 0.7/0.1/0.2.
